Image processing method that detects a particular moving object from an image by a fixed camera and tracking is
noticed in various fields and it is a very important subject. In this paper, we propose a moving object tracking method
that can cope with change of the area accompany the random walk movement of the moving object oneself and change
of the brightness arise from change of the environmental such as a masking or change of the illumination. Proposal
method can be robust processing for change of the illumination based on Orientation Code Matching that is
demonstrated that is robust for the masking or change of the illumination. And, using Motion Vector derived from a
continuity of the random walk model motion, under the condition that there are similar walk models, it can discriminate
the walk model and individually tracking. Through the some experiment, this paper inspects the effectiveness of our
This paper treats the navigation problem of a mobile robot based on vision information and ultrasonic data. In
our method, by calculating the optical flow on the images, the mobile robot can detect obstacles which exist
ahead of it, further avoiding the area of obstacles, it can make the optimal trajectory to the final goal. Then, in
order to generate the optimal trajectory, the distance between a mobile robot and obstacle is needed and then
is obtained by evaluating a function with ultrasonic information. Through some experiments, we show how our
proposed method is effective.
Recently, developing of image processing method which enables to track to moving objects on time series images
taken by a fixed camera is one of important subjects in the field of machine vision. Here, we try to consider
influences by change in brightness and change of region caused by moving objects, respectively. In this paper,
we introduce a new tracking method which can be reduced the influences by those changes. First, we use Radial
Reach Filter in order to detect the moving objects. In addition, the moving objects can be tracked by an image
processing based on information obtained by applying RRF and block division. Further, we propose a method in
the case that changes size of moving object by time progress. Finally, through experiments we show the validity
of our proposed method.
In this paper, we develop an observation device to measure a 3D position for a moving object by using a laser range finder and a CCD camera. Then, we propose a new method for the object recognition and the tracking control, respectively.
As for the recognition, we use a special mark which is called the cross mark. For the tracking control, we construct PID control with an extended Kalman filter to realize control system without delay. Through some experiments, we verify performance of observation device and show availability of our proposed method.
This paper introduces tracking control system with visual feedback to a moving object by using the measurement device which we developed. In order to recognize the moving object, we use two method, using cross-shape mark and Orientation Code Matching (OCM). And the measurement device is constructed PID control system with Extended Kalman Filter in order to track to object. Through the several experiments, we verify the percormance of recognition and tracking.
In this paper, we propose a new method of object detection. In the past, there are various methods of object detection. Especially, the method of the background subtraction has the effectiveness. However, the methods based on brightness differences are easily influenced by change in lighting condition. In this paper, we use Radial Reach Filter (RRF). RRF is called as the effective method of the change in lighting conditions. However, RRF is not considered change that caused by moving objects on the background image. Then, we propose the new method of object detection that considered motion of the moving objects on the background image. And, we verify the effectiveness by the experiments using a time series image.
The problem we studied in this paper is to understand to what extent motion and shape parameters can be estimated from an optical flow generated on the image plane. The optical flow is generated by projecting the phase portrait of a class of motion of object in R3 onto the image plane in R2. Here, the class of motion of object we considered is a two-dimensional plane undergoing Riccati motion. The projection models are perspective and orthographic projections. Namely, in this paper, we show several results on the problem of parameter estimation of Riccati motion under the two projection models. One of results is that the parameters of Riccati motion can be estimated up to choice of a sign. Thus, for all practical purposes, when the relative position of the object undergoing Riccati motion is known, motion and shape parameters can be recovered uniquely. This fact is in sharp contrast with existing known result in the literature about affine motion under perspective projection where parameters can only be recovered up to a possible depth ambiguity.
In recent years, three-dimensional measurement in machine vision is applied to various places, such as a production line. When applying to a production line, in order to raise its reliability and cost performance, it is indispensable to measure the three-dimensional position of an object at high speed and with high precision. Then, in order to measure the position of an object, we propose the new
three-dimensional measurement technique which combined the single
light stripe method and the relative stereo method in this paper.
Further, we will show that this technique can measure the
three-dimensional position of all the objects projected in the stereo images at high speed and with high precision. Finally, we show results of an evaluation experiment for the measurement technique.